Can social media usage of scientific literature predict journal indices of AJG,SNIP and JCR? An altmetric study of economics

Scientometrics - Altmetrics are often praised as an alternative or complement to classic bibliometric metrics, especially in the social sciences discipline. However, empirical investigations of...     

Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks

This paper investigates the design of fault-tolerant TDMA-based data aggregation scheduling (DAS) protocols for wireless sensor networks (WSNs). DAS is a fundamental pattern of communication in wireless sensor networks where sensor nodes aggregate and relay data to a sink node. However, any such DAS protocol needs to be cognisant of the fact that crash failures can occur. We make the following contributions: (i) we identify a necessary condition to solve the DAS problem, (ii) we introduce a strong and weak version of the DAS problem, (iii) we show several impossibility results due to the crash failures, (iv) we develop a modular local algorithm that solves stabilising weak DAS and (v) we show, through simulations and an actual deployment on a small testbed, how specific instantiations of parameters can lead to the algorithm achieving very efficient stabilisation.     

Infrared spectroscopic investigation of tannins

Infrared spectroscopy is suggested as a diagnostic method for the characterisation and qualitative estimation of the two classes of tannins. Gallic acid, tannic acid and chebulinic acid have been taken as model compounds for the hydrolysable and catechin for the condensed tannins. The former class is marked by the presence of strong absorption maxima at 1710 – 35 cm^?1. The two classes have characteristic pattern of absorption, from which it is possible to characterise the particular type of tannin.     

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

Magnetic Resonance Materials in Physics, Biology and Medicine - The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of...     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.     

Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

A substitution box (S-Box) is a crucial component of contemporary cryptosystems that provide data protection in block ciphers. At the moment, chaotic maps are being created and extensively used to generate these S-Boxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical attempts. In this paper, the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking approach. The projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their working makes both of these dynamic in nature. The tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic fort. Values of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique S-Box. Comparative analysis and exploration confirmed that the projected chaotic map exhibits a significant amount of chaotic complexity. The security assessment in terms of bijectivity, nonlinearity, bits independence, strict avalanche, linear approximation probability, and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several assaults. The proposed S-Box’s cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security applications. The comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.     

Authentication and authorization infrastructure for Grids—issues,technologies, trends and experiences

Authentication and authorization for Grids is a challenging security issue. In this paper, key issues for the establishment of Grid authentication and authorization infrastructures are discussed, and an overview of major Grid authentication and authorization technologies is presented. Related to this, recent developments in Grid authentication and authorization infrastructures suggest adoption of the Shibboleth technology which offers advantages in terms of usability, confidentiality, scalability and manageability. When combined with advanced authorization technologies, Shibboleth-based authentication and authorization infrastructures provide role-based, fine-grained authorization. We share our experience in constructing a Shibboleth-based authentication and authorization infrastructure and believe that such infrastructure provides a promising solution for the security of many application domains.     

Identification of pole-like structures from mobile lidar data of complex road environment

Pole-like structures (PLSs) located in road environment are important roadway assets. They play a vital role in road safety inspection and road planning. The use of light detection and ranging (lidar) based mobile mapping technology for mapping of PLSs is an important area of research as it holds the potential for automation. Point cloud data of rural, peri-urban, and urban road environment are used in this study, which pose special challenge in view of the complexity of terrain, unlike well-planned roads, which have been the subject of interest in existing literature for identification of PLSs. A new five-step method is proposed in this article. The first two steps, i.e. ground filtering and voxelization of filtered non-ground points, are used for data size reduction. Next three steps are used to extract PLSs from reduced data. The proposed method was tested on point cloud data of three test sites having different levels of complexities. PLSs including partially occluded pole, tilted pole, pole situated very close to other objects, and vertical pole attached to tilted pole were accurately identified. Average correctness and completeness, respectively of 92.6% and 94.9%, were achieved in three different complex test sites, i.e. urban, peri-urban, and rural sites, respectively. Computation complexity shows that our proposed method delivers fast and computationally efficient solution for identifying the PLSs from volumetric mobile lidar point cloud. Impact of PLSs on road safety and road planning is also addressed for these selected test sites.